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The State-of-the-Art Progress in Cloud Detection, Identification, and Tracking Approaches: A Systematic Review

Author

Listed:
  • Manisha Sawant

    (Department of Electronics and Communication Engineering, Visvesvaraya National Institute of Technology, Nagpur 440010, India)

  • Mayur Kishor Shende

    (Department of Computer Science and Engineering, Defense Institute of Advanced Technology, Pune 411025, India)

  • Andrés E. Feijóo-Lorenzo

    (Departamento de Enxeñería Eléctrica, EEI, Campus de Lagoas-Marcosende, Universidade de Vigo, 36310 Vigo, Spain)

  • Neeraj Dhanraj Bokde

    (Department of Civil and Architectural Engineering, Aarhus University, 8000 Aarhus, Denmark)

Abstract

A cloud is a mass of water vapor floating in the atmosphere. It is visible from the ground and can remain at a variable height for some time. Clouds are very important because their interaction with the rest of the atmosphere has a decisive influence on weather, for instance by sunlight occlusion or by bringing rain. Weather denotes atmosphere behavior and is determinant in several human activities, such as agriculture or energy capture. Therefore, cloud detection is an important process about which several methods have been investigated and published in the literature. The aim of this paper is to review some of such proposals and the papers that have been analyzed and discussed can be, in general, classified into three types. The first one is devoted to the analysis and explanation of clouds and their types, and about existing imaging systems. Regarding cloud detection, dealt with in a second part, diverse methods have been analyzed, i.e., those based on the analysis of satellite images and those based on the analysis of images from cameras located on Earth. The last part is devoted to cloud forecast and tracking. Cloud detection from both systems rely on thresholding techniques and a few machine-learning algorithms. To compute the cloud motion vectors for cloud tracking, correlation-based methods are commonly used. A few machine-learning methods are also available in the literature for cloud tracking, and have been discussed in this paper too.

Suggested Citation

  • Manisha Sawant & Mayur Kishor Shende & Andrés E. Feijóo-Lorenzo & Neeraj Dhanraj Bokde, 2021. "The State-of-the-Art Progress in Cloud Detection, Identification, and Tracking Approaches: A Systematic Review," Energies, MDPI, vol. 14(23), pages 1-26, December.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:23:p:8119-:d:694620
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    References listed on IDEAS

    as
    1. Neeraj Bokde & Andrés Feijóo & Nadhir Al-Ansari & Siyu Tao & Zaher Mundher Yaseen, 2020. "The Hybridization of Ensemble Empirical Mode Decomposition with Forecasting Models: Application of Short-Term Wind Speed and Power Modeling," Energies, MDPI, vol. 13(7), pages 1-23, April.
    2. Manisha Sawant & Sameer Thakare & A. Prabhakara Rao & Andrés E. Feijóo-Lorenzo & Neeraj Dhanraj Bokde, 2021. "A Review on State-of-the-Art Reviews in Wind-Turbine- and Wind-Farm-Related Topics," Energies, MDPI, vol. 14(8), pages 1-30, April.
    3. Neeraj Bokde & Andrés Feijóo & Daniel Villanueva & Kishore Kulat, 2019. "A Review on Hybrid Empirical Mode Decomposition Models for Wind Speed and Wind Power Prediction," Energies, MDPI, vol. 12(2), pages 1-42, January.
    4. Walter Richardson & Hariharan Krishnaswami & Rolando Vega & Michael Cervantes, 2017. "A Low Cost, Edge Computing, All-Sky Imager for Cloud Tracking and Intra-Hour Irradiance Forecasting," Sustainability, MDPI, vol. 9(4), pages 1-17, March.
    5. Alonso, J. & Batlles, F.J., 2014. "Short and medium-term cloudiness forecasting using remote sensing techniques and sky camera imagery," Energy, Elsevier, vol. 73(C), pages 890-897.
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    Cited by:

    1. Zhang, Liwenbo & Wilson, Robin & Sumner, Mark & Wu, Yupeng, 2025. "Transfer learning in very-short-term solar forecasting: Bridging single site data to diverse geographical applications," Applied Energy, Elsevier, vol. 377(PC).

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